{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import json\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = json.load(open('data/round1_train_0907.json'))\n",
    "test = json.load(open('data/round1_test_0907.json'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def json2df(dic):\n",
    "    D = {'passage': list(), 'question': list(), 'answer': list()}\n",
    "    for d in dic:\n",
    "        for qa in d['annotations']:\n",
    "            D['passage'].append(d['text'])\n",
    "            D['question'].append(qa['Q'])\n",
    "            D['answer'].append(qa['A'])\n",
    "            \n",
    "    return pd.DataFrame(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = json2df(train)\n",
    "df_test = json2df(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 简单查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passage</th>\n",
       "      <th>question</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"胆石症的治疗应区别不同情况分别处理，无症状胆囊结石可不作治疗，但应定期观察并注意良好的饮食...</td>\n",
       "      <td>什么类型的胆囊结石可不作治疗？</td>\n",
       "      <td>无症状胆囊结</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"胆石症的治疗应区别不同情况分别处理，无症状胆囊结石可不作治疗，但应定期观察并注意良好的饮食...</td>\n",
       "      <td>胆石症的治疗应注意什么？</td>\n",
       "      <td>应区别不同情况分别处理</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"胆石症的治疗应区别不同情况分别处理，无症状胆囊结石可不作治疗，但应定期观察并注意良好的饮食...</td>\n",
       "      <td>胆管结石宜采用什么样的治疗方式？</td>\n",
       "      <td>以手术为主的综合治疗</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>反佐配伍的典范，始见于张仲景《伤寒杂病论》，其中记载“干呕、吐涎沫、头痛者吴茱萸汤主之”。患...</td>\n",
       "      <td>“干呕、吐涎沫、头痛者吴茱萸汤主之”这句话曾出现在哪本医学巨著中？</td>\n",
       "      <td>《伤寒杂病论》</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>反佐配伍的典范，始见于张仲景《伤寒杂病论》，其中记载“干呕、吐涎沫、头痛者吴茱萸汤主之”。患...</td>\n",
       "      <td>《伤寒杂病论》的作者是谁？</td>\n",
       "      <td>张仲景</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             passage  \\\n",
       "0  \"胆石症的治疗应区别不同情况分别处理，无症状胆囊结石可不作治疗，但应定期观察并注意良好的饮食...   \n",
       "1  \"胆石症的治疗应区别不同情况分别处理，无症状胆囊结石可不作治疗，但应定期观察并注意良好的饮食...   \n",
       "2  \"胆石症的治疗应区别不同情况分别处理，无症状胆囊结石可不作治疗，但应定期观察并注意良好的饮食...   \n",
       "3  反佐配伍的典范，始见于张仲景《伤寒杂病论》，其中记载“干呕、吐涎沫、头痛者吴茱萸汤主之”。患...   \n",
       "4  反佐配伍的典范，始见于张仲景《伤寒杂病论》，其中记载“干呕、吐涎沫、头痛者吴茱萸汤主之”。患...   \n",
       "\n",
       "                            question       answer  \n",
       "0                    什么类型的胆囊结石可不作治疗？       无症状胆囊结  \n",
       "1                       胆石症的治疗应注意什么？  应区别不同情况分别处理  \n",
       "2                   胆管结石宜采用什么样的治疗方式？   以手术为主的综合治疗  \n",
       "3  “干呕、吐涎沫、头痛者吴茱萸汤主之”这句话曾出现在哪本医学巨著中？      《伤寒杂病论》  \n",
       "4                      《伤寒杂病论》的作者是谁？          张仲景  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>passage</th>\n",
       "      <th>question</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...</td>\n",
       "      <td></td>\n",
       "      <td>橄榄的果实中含有蛋白质、脂肪、碳水化合物以及钙.磷、铁等。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...</td>\n",
       "      <td></td>\n",
       "      <td>清肺，利咽，生津：解毒</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...</td>\n",
       "      <td></td>\n",
       "      <td>青子</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...</td>\n",
       "      <td></td>\n",
       "      <td>主治咽喉肿痛，烦渴，咳嗽咯血以及细菌性痢疾、癫痫等，还能解除河豚毒以及酒毒。</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>黄帝说：我想听听运气学说是怎样创始的。岐伯说：你提这个问题很高明的啊！我曾看到《太始天元册》...</td>\n",
       "      <td></td>\n",
       "      <td>赤色的天气，经过牛、女二宿及西北方的戊分；黄色的天气，经过心、尾二宿及东南方的已分；青色的天...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             passage question  \\\n",
       "0  \"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...            \n",
       "1  \"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...            \n",
       "2  \"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...            \n",
       "3  \"橄榄，又名青果、白榄，为橄榄科植物橄榄的果实，产广东、广西、福建等地。宋朝大文学家苏东坡称...            \n",
       "4  黄帝说：我想听听运气学说是怎样创始的。岐伯说：你提这个问题很高明的啊！我曾看到《太始天元册》...            \n",
       "\n",
       "                                              answer  \n",
       "0                      橄榄的果实中含有蛋白质、脂肪、碳水化合物以及钙.磷、铁等。  \n",
       "1                                        清肺，利咽，生津：解毒  \n",
       "2                                                 青子  \n",
       "3             主治咽喉肿痛，烦渴，咳嗽咯血以及细菌性痢疾、癫痫等，还能解除河豚毒以及酒毒。  \n",
       "4  赤色的天气，经过牛、女二宿及西北方的戊分；黄色的天气，经过心、尾二宿及东南方的已分；青色的天...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18478"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4373"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 剔除空白字符\n",
    "df_train = df_train.applymap(lambda x: re.sub(r'\\s', '', x))\n",
    "df_train = df_train.applymap(lambda x: re.sub(r'\\\\n', '', x))\n",
    "df_test = df_test.applymap(lambda x: re.sub(r'\\s', '', x))\n",
    "df_test = df_test.applymap(lambda x: re.sub(r'\\\\n', '', x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 剔除带括号的英文\n",
    "func = lambda m: '' if len(m.group(0)) > 5 else m.group(0)\n",
    "df_train = df_train.applymap(lambda x: re.sub(r'\\([A-Za-z]+\\)', func, x))\n",
    "df_train = df_train.applymap(lambda x: re.sub(r'（[A-Za-z]+）', func, x))\n",
    "df_test = df_test.applymap(lambda x: re.sub(r'\\([A-Za-z]+\\)', func, x))\n",
    "df_test = df_test.applymap(lambda x: re.sub(r'（[A-Za-z]+）', func, x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 文本长度分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## describe\n",
    "通过数据描述可以看到：\n",
    "* 训练集共18478条文本数据；\n",
    "    * 篇章文本平均358个字符，最短的句子长度为40，最长的句子长度为511，其中75%以下的数据长度在453以下；\n",
    "    * 问题文本平均14个字符，最短的句子长度为5，最长的句子长度为130，其中75%以下的数据长度在17以下；\n",
    "    * 答案文本平均47个字符，最短的句子长度为1，最长的句子长度为508，其中75%以下的数据长度在58以下。\n",
    "* 测试集共4373条文本数据；\n",
    "    * 篇章文本平均353个字符，最短的句子长度为46，最长的句子长度为511，其中75%以下的数据长度在449以下；\n",
    "    * 答案文本平均48个字符，最短的句子长度为1，最长的句子长度为508，其中75%以下的数据长度在59以下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18478.000000\n",
       "mean       358.024678\n",
       "std        108.896379\n",
       "min         40.000000\n",
       "25%        283.000000\n",
       "50%        376.000000\n",
       "75%        453.000000\n",
       "max        511.000000\n",
       "Name: passage, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.passage.map(lambda x: len(x.strip())).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18478.000000\n",
       "mean        14.386947\n",
       "std          6.500708\n",
       "min          5.000000\n",
       "25%         10.000000\n",
       "50%         13.000000\n",
       "75%         17.000000\n",
       "max        130.000000\n",
       "Name: question, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.question.map(lambda x: len(x.strip())).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    18478.000000\n",
       "mean        47.635675\n",
       "std         50.482854\n",
       "min          1.000000\n",
       "25%         19.000000\n",
       "50%         34.000000\n",
       "75%         58.000000\n",
       "max        508.000000\n",
       "Name: answer, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.answer.map(lambda x: len(x.strip())).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4373.000000\n",
       "mean      353.149325\n",
       "std       111.697666\n",
       "min        46.000000\n",
       "25%       273.000000\n",
       "50%       374.000000\n",
       "75%       449.000000\n",
       "max       511.000000\n",
       "Name: passage, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.passage.map(lambda x: len(x.strip())).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4373.000000\n",
       "mean       48.533501\n",
       "std        53.045020\n",
       "min         1.000000\n",
       "25%        19.000000\n",
       "50%        34.000000\n",
       "75%        59.000000\n",
       "max       508.000000\n",
       "Name: answer, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.answer.map(lambda x: len(x.strip())).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 直方图\n",
    "通过直方图可以看到：\n",
    "* 篇章文本长度在100以下的数据较少，长度区间400-500的数据占比较大。\n",
    "* 问题文本长度主要集中在5-20这个区间，长度40以上的数据较少。\n",
    "* 答案文本长度主要集中在1-100区间，长度200以上的数据较少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f294f6e6990>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "ax = sns.distplot(df_train.passage.map(lambda x: len(x.strip())).values, bins=100)\n",
    "ax = sns.distplot(df_test.passage.map(lambda x: len(x.strip())).values, bins=100)\n",
    "plt.xlim([0, max(max(df_train.passage.map(lambda x: len(x.strip())).values), max(df_test.passage.map(lambda x: len(x.strip())).values))])\n",
    "plt.xlabel(\"length of sample\")\n",
    "plt.ylabel(\"prob of sample\")\n",
    "plt.legend(['train_len','test_len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f294cb37790>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "ax = sns.distplot(df_train.question.map(lambda x: len(x.strip())).values, bins=100)\n",
    "plt.xlim([0, max(df_train.question.map(lambda x: len(x.strip())).values)])\n",
    "plt.xlabel(\"length of sample\")\n",
    "plt.ylabel(\"prob of sample\")\n",
    "plt.legend(['train_len'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f294caf2650>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "ax = sns.distplot(df_train.answer.map(lambda x: len(x.strip())).values, bins=100)\n",
    "ax = sns.distplot(df_test.answer.map(lambda x: len(x.strip())).values, bins=100)\n",
    "plt.xlim([0, max(max(df_train.answer.map(lambda x: len(x.strip())).values), max(df_test.answer.map(lambda x: len(x.strip())).values))])\n",
    "plt.xlabel(\"length of sample\")\n",
    "plt.ylabel(\"prob of sample\")\n",
    "plt.legend(['train_len','test_len'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 同分布验证\n",
    "* 篇章的P值为0.03，比指定的显著水平（假设为5%）小，我们认为二者不是同分布。\n",
    "* 答案的P值为0.84，比指定的显著水平（假设为5%）小，我们认为二者同分布。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ks_2sampResult(statistic=0.023524541901201457, pvalue=0.039927605323010484)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "scipy.stats.ks_2samp(df_train.passage.map(lambda x: len(x.strip())).values, df_test.passage.map(lambda x: len(x.strip())).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ks_2sampResult(statistic=0.01037905238055789, pvalue=0.8407291806931478)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scipy.stats.ks_2samp(df_train.answer.map(lambda x: len(x.strip())).values, df_test.answer.map(lambda x: len(x.strip())).values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析总结\n",
    "* 训练数据量适中，不是很大数据量，但是也不算少。\n",
    "* 文本长度：篇章文本最大，其次是答案文本，最后是问题文本。\n",
    "* 如果只看答案文本，那它的长度分布应该是同分布。\n",
    "* 若要将篇章、问题和答案拼接进行训练，则需要对其进行文本截断；\n",
    "    * 问题是要预测的部分，并且长度不是太长，所以可以不进行截断；\n",
    "    * 答案是从篇章中截取的，可以适当截取短一点；\n",
    "    * 篇章在硬件资源允许的范围内，可以尽量截取长一点。"
   ]
  }
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